The Study of Cause & Effect

• Causal inference is the process of estimating the mechanisms by which changes in variables (causes) lead to changes in an outcome (the effect).
• It seeks to establish a cause-and-effect that is generalisable and substantively meaningful in the real world.
• It’s more than just Randomised Controlled Trials!

Moving Beyond “Associations”

• We are often interested in questions of “cause”, but we use methods (and language) more fit for description.
• It is very common to see the term “association” used, to avoid claiming the outcome has been “caused” by our predictors (I am guilty of this).
  • But association still implies some causal effect (Haber et al. 2022).
  • And the recommendations and implications of the research clearly indicate a belief that the findings are causal!

Schrödinger’s Causal Inference

Our results suggest that “Schrödinger’s causal inference,” — where studies avoid stating (or even explicitly deny) an interest in estimating causal effects yet are otherwise embedded with causal intent, inference, implications, and recommendations — is common.

— Haber et al. (2022)

More Than a Name

• While “causal inference” has become the popular terminology, these ideas are not entirely new.
• However, the approach (DAGs, explicit processes for drawing causal conclusions) has made the old methods more precise.
• Much of causal inference focuses on methods for making causal claims possible when dealing with observational data.

Visualising Causal Models Using DAGs

The Challenge

• It is sometimes difficult to tell whether you are dealing with description, prediction, or explanation.
• This is because methods like regression are appropriate for all three and the way we talk about each often blurs the lines [barrett2024].

Description - What Happened?

• “Description is using data to provide a quantitative summary of certain features of the world.” (Hernán, Hsu, and Healy 2019)
• Description is concerned with understanding what happened.
• Counting events, estimating central tendencies (mean, median, mode) and variance, and visualising distributions.
• Often used in combination with prediction and causal inference.

Counting Things is Good, Actually

• Description is frequently treated as a lesser task, but I think this is fundamentally wrong.
• Counting is one of the best things we can do with data (Barrett, McGowan, and Gerke 2024)!
• It can be very valuable when the quantity is extremely important.
• It can also be sufficient if we already know a lot about it from previous causal work.

Don’t Mistake Description for Explanation!

• It is very easy to fall into the trap of treating description as causal, or using causal terms when discussing descriptive findings.

Prediction - What Will Happen?

• “Prediction is using data to map some features of the world (the inputs) to other features of the world (the outputs).” (Hernán, Hsu, and Healy 2019)
• Prediction focuses on identifying what will happen in the future.
  • How many A&E attendances will occur in the next hour, day, week, or month?
  • What is the probability of an individual suffering a health episode?
• Machine learning is very good at answering these kinds of questions.

Causal Inference - What Was the Cause?

• Causal inference tries to identify and quantify cause and effect.
  • What are the primary causal factors that define how long someone will wait in A&E?
  • What are the environmental factors that lead to someone developing cancer?
  • Do virtual wards reduce the quantity of hospital attendances an individual experiences?

The Differences Between Predictive & Causal Models

• Causal models are interested in unbiased estimates, prediction focuses on reducing error.
• Predictive models can (and should) include plenty of correlations
• With causal models, fewer variables and focus should be on causal mechanism
• A causal model can identify a small but meaningful effect that would not predict the outcome on its own and this would still be useful!

Conclusion

• Understanding the type of question you’re asking will define the methods you will use to find the answer.
• No approach is inherently more valuable, but they serve specific purposes!
• Treating description as demonstrating causality is problematic.
• Treating prediction and explanation as equivalent will lead to building models that are not fit for the task.

Further Resources

• Causal Inference in R
• Causal Inference for the Brave and True
• Causal Inference: The Mixtape
• Causal Inference: What If
• The Effect: An Introduction to Research Design and Causality
• The Book of Why: The New Science of Cause and Effect